Professor Peter Tino

School of Computer Science
Professor of Complex and Adaptive Systems

Contact details

Telephone: +44 (0) 121 414 8558
Email: p.tino@cs.bham.ac.uk

Address: School of Computer Science
University of Birmingham
Edgbaston
Birmingham
B15 2TT
UK

Peter Tiňo is a Professor of Complex and Adaptive Systems at the School of Computer Science at the University of Birmingham. He is the author of over 160 research articles in the areas of dynamical systems, machine learning, natural computation and fractal geometry. Peter has been awarded three outstanding Journal Paper of the Year awards and the Head of School's Excellence in Teaching Award.

Professor Tiňo is a co-supervisor of ECOLE, an Innovative Training Network (ITN) for early stage researchers (ESRs) funded by the EU’s Horizon 2020 research and innovation program under grant agreement No.766186. It is based on novel synergies between nature inspired optimisation and machine learning. The training programme will be targeted at the automotive industry and ESRs employed on the program will be provided with the transferable skills necessary for thriving careers in emerging and rapidly developing industrial areas.

Please follow the link below to find out more about Professor Tiňo's work:

Professor Tiňo's-personal web page

Biography

After finishing his university studies in Slovakia, Peter managed to secure Fulbright scholarship to finalize his PhD work on dynamical systems at the NEC Research Institute in Princeton, USA. Returning back home, after a brief spell at the Slovak University of Technology, he worked as a research fellow in Vienna at the Austrian Research Institute for AI on predictive machine learning models for option pricing and in Birmingham at Aston University within the Neural Computation Research Group on probabilistic modelling. He joined the School of Computer Science, the University of Birmingham in 2003, where he has been ever since. Peter still likes to span a range of disciplines from machine learning and natural computation to complex systems. He loves the challenges brought up by truly cross-disciplinary work and enjoys collaborating with colleagues from around the world. Peter likes teaching. If you prefer the old-style teaching with pen and whiteboard, you are very welcome to his lectures! He believes that everything is teachable if the story behind the material is communicated in the right way.

Postgraduate supervision

Peter has supervised and co-supervised 16 PhD students to successful completion of their studies. He currently supervises and co-supervises 13 research students.

Research

Peter is interested in theory and interdisciplinary applications of machine learning, probabilistic modelling and dynamical systems.

Publications

Recent publications

Article

Tino, P 2020, 'Dynamical systems as temporal feature spaces', Journal of Machine Learning Research, vol. 21, no. 44, pp. 1-42. <http://jmlr.org/papers/v21/19-589.html>

Pfannschmidt, L, Jacob, J, Hinder, F, Biehl, M, Tino, P & Hammer, B 2020, 'Feature relevance determination for ordinal regression in the context of feature redundancies and privileged information', Neurocomputing. https://doi.org/10.1016/j.neucom.2019.12.133

Tang, F, Fan, M & Tino, P 2020, 'Generalized Learning Riemannian Space Quantization: a Case Study on Riemannian Manifold of SPD Matrices', IEEE Transactions on Neural Networks and Learning Systems.

Alzheimer’s Disease Neuroimaging Initiative, Giorgio, J, Landau, S, Jagust, W, Tino, P & Kourtzi, Z 2020, 'Modelling prognostic trajectories of cognitive decline due to Alzheimer's disease', NeuroImage: Clinical, vol. 26, 102199, pp. 1-14. https://doi.org/10.1016/j.nicl.2020.102199

Pauli, R, Tino, P, Rogers, JC, Baker, R, Clanton, R, Birch, P, Brown, A, Daniel, G, Ferreira, L, Grisley, L, Kohls, G, Baumann, S, Bernhard, A, Martinelli, A, Ackermann, K, Lazaratou, H, Tsiakoulia, F, Bali, P, Oldenhof, H, Jansen, L, Smaragdi, A, Gonzalez-Madruga, K, Gonzalez-Torres, MA, González de Artaza-Lavesa, M, Steppan, M, Vriends, N, Bigorra, A, Siklósi, R, Ghosh, S, Bunte, K, Dochnal, R, Hervas, A, Stadler, C, Fernández-Rivas, A, Fairchild, G, Popma, A, Dikeos, D, Konrad, K, Herpertz-Dahlmann, B, Freitag, CM, Rotshtein, P & De Brito, S 2020, 'Positive and negative parenting in conduct disorder with high versus low levels of callous-unemotional traits', Development and Psychopathology. https://doi.org/10.1017/S0954579420000279

Chong, SY, Tino, P & He, J 2019, 'Coevolutionary systems and PageRank', Artificial Intelligence, vol. 277, 103164. https://doi.org/10.1016/j.artint.2019.103164

Karlaftis, VM, Giorgio, J, Vértes, PE, Wang, R, Shen, Y, Tino, P, Welchman, A & Kourtzi, Z 2019, 'Multimodal imaging of brain connectivity reveals predictors of individual decision strategy in statistical learning', Nature Human Behaviour, vol. 3, no. 3, pp. 297-307. https://doi.org/10.1038/s41562-018-0503-4

Tino, P 2018, 'Asymptotic Fisher Memory of Randomized Linear Symmetric Echo State Networks', Neurocomputing, vol. 298, pp. 4-8. https://doi.org/10.1016/j.neucom.2017.11.076

Bunte, K, Smith, D, Chappell, MJ, Hassan-Smith, Z, Tomlinson, J, Arlt, W & Tino, P 2018, 'Learning pharmacokinetic models for in vivo glucocorticoid activation', Journal of Theoretical Biology, vol. 455, pp. 222-231. https://doi.org/10.1016/j.jtbi.2018.07.025

Rupawala, M, Dehghani, H, Lucas, SJE, Tino, P & Cruse, D 2018, 'Shining a Light on Awareness: A Review of Functional Near-Infrared Spectroscopy for Prolonged Disorders of Consciousness', Frontiers in neurology, vol. 9, 350. https://doi.org/10.3389/fneur.2018.00350

Schleif, F-M, Gisbrecht, A & Tino, P 2018, 'Supervised low rank indefinite kernel approximation using minimum enclosing balls', Neurocomputing. https://doi.org/10.1016/j.neucom.2018.08.057

Chong, SY, Tino, P, He, J & Yao, X 2017, 'A New Framework for Analysis of Coevolutionary Systems - Directed Graph Representation and Random Walks', Evolutionary Computation. https://doi.org/10.1162/evco_a_00218

Shen, Y, Tino, P & Tsaneva-Atanasova, K 2017, 'Classification framework for partially observed dynamical systems', Physical Review E, vol. 95, no. 4, 043303. https://doi.org/10.1103/PhysRevE.95.043303

Conference contribution

Friess, S, Tino, P, Menzel, S, Sendhoff, B & Yao, X 2020, Learning Across Problem Instances: Improving Sampling in Evolution Strategies through Mixture-based Distributions. in Learning Across Problem Instances: Improving Sampling in Evolution Strategies through Mixture-based Distributions.

Friess, S, Tino, P, Menzel, S, Sendhoff, B & Yao, X 2019, Learning Transferable Variation Operators in a Continuous Genetic Algorithm. in 2019 IEEE Symposium Series on Computational Intelligence (SSCI 2019)., 9002976, Institute of Electrical and Electronics Engineers (IEEE), pp. 2027-2033, 2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019, Xiamen, China, 6/12/19. https://doi.org/10.1109/SSCI44817.2019.9002976

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